Kernels on Prolog Proof Trees:Statistical Learning in the ILP Setting

Abstract

An example-trace is a sequence of steps taken by a program on
a given example input. Different approaches exist in order to
exploit example-traces for learning, all explicitly inferring a
target program from positive and negative traces.
We generalize such idea by developing similarity measures betweeen traces
in order to learn to discriminate between positive and
negative ones. This allows to combine the expressiveness of
inductive logic programming in representing knowledge to the statistical
properties of kernel machines. Logic programs will be used to generate
proofs of given visitor programs which exploit the available background
knowledge, while kernel machines will be employed to learn from such proofs.